Lynch, T., & Ghergulescu, I. (2017). Large Scale Evaluation of Learning Flow. In 2017 IEEE 17th International Conference on Advanced Learning Technologies (ICALT) (pp. 62–64).
Personalized and adaptive learning is the fastest growing field in e-learning. Adaptive e-learning systems are typically well suited for real-world heterogeneous users, which exhibit different levels of motivation and knowledge. Furthermore, students learn best when they are in flow, i.e. when the level of difficulty is perfectly adjusted to their individual abilities. A personalized, adaptive, and intelligent learning environment can provide each student with this learning experience. In this paper, we present a large-scale evaluation of learning in flow within an adaptive and personalized system, the Adaptemy system. The paper presents the results of two studies: an objective study with 7,614 Irish secondary school students in math classes assessing their learning flow, and a subjective study with 80 students assessing their perceived learning experience. The results from the objective study show that 88% of the students worked within the flow channel. In the subjective study, 70% of students reported a perceived improvement in their math skills after the exercise studying with the adaptive and intelligent learning system.